Characterizing a vehicle collision

ABSTRACT

Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/928,314, filed Jul. 14, 2020, which is a continuation of U.S.application Ser. No. 16/456,077, filed Jun. 28, 2019, which claims thepriority benefit under 35 U.S.C. § 119 of Spanish Application No.P201830655, filed Jun. 29, 2018, the entire contents of each is hereinincorporated by reference.

BACKGROUND

Some organizations, including commercial enterprises or otherorganizations, own and/or operate a fleet of vehicles. For example, acommercial enterprise that provides goods or services to customers atcustomers' homes (e.g., pest control services, or grocery deliveryservices) may own and/or operate a fleet of vehicles used by employeesand/or contractors to travel to the customers' homes.

These organizations often desire to know when one of their vehicles hasbeen involved in a collision, so the organization can respondappropriately and effectively. The organization may want to knowpromptly that a collision has occurred so it can attempt to contact theemployee/contractor who was operating the vehicle and determine whetherthe employee/contractor, or any other person, has been injured and sothat emergency services can be dispatched if needed. The organizationmay also want to know promptly that a collision has occurred so it canpromptly begin investigating the collision and determine whether theorganization is likely incur any liability for the collision and, if so,so that it can begin addressing the collision appropriately.

SUMMARY

In one embodiment, there is provided a method comprising, in response toobtaining information regarding a potential collision between a vehicleand an object, obtaining, for a time period extending before and after atime of the potential collision, data describing the vehicle during thetime period, the data describing the vehicle during the time periodincluding, for each time of a plurality of times within the time period,acceleration data indicating acceleration of the vehicle at the time andspeed of the vehicle at the time, classifying, using at least onetrained classifier, the data describing the vehicle into at least one ofa plurality of classes, each class of the plurality of classes beingassociated with whether a collision occurred, determining whether thepotential collision is likely to have been a collision based at least inpart on the at least one class identified in the classifying.

In another embodiment, there is provided at least one non-transitorycomputer-readable storage medium having encoded thereon executableinstructions that, when executed by at least one processor, cause the atleast one processor to carry out a method. The method comprises, inresponse to obtaining information regarding a potential collisionbetween a vehicle and an object, obtaining, for a time period extendingbefore and after a time of the potential collision, data describing thevehicle during the time period, the data describing the vehicle duringthe time period including, for each time of a plurality of times withinthe time period, acceleration data indicating acceleration of thevehicle at the time and speed of the vehicle at the time, classifying,using at least one trained classifier, the data describing the vehicleinto at least one of a plurality of classes, each class of the pluralityof classes being associated with whether a collision occurred, anddetermining whether the potential collision is likely to have been acollision based at least in part on the at least one class identified inthe classifying.

In a further embodiment, there is provided an apparatus comprising atleast one processor and at least one storage medium having encodedthereon executable instructions that, when executed by the at least oneprocessor, cause the at least one processor to carry out a method. Themethod comprises, in response to obtaining information regarding apotential collision between a vehicle and an object, obtaining, for atime period extending before and after a time of the potentialcollision, data describing the vehicle during the time period, the datadescribing the vehicle during the time period including, for each timeof a plurality of times within the time period, acceleration dataindicating acceleration of the vehicle at the time and speed of thevehicle at the time, classifying, using at least one trained classifier,the data describing the vehicle into at least one of a plurality ofclasses, each class of the plurality of classes being associated withwhether a collision occurred, and determining whether the potentialcollision is likely to have been a collision based at least in part onthe at least one class identified in the classifying.

The foregoing is a non-limiting summary of the invention, which isdefined by the attached claims.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a sketch of an illustrative system with which some embodimentsmay operate;

FIG. 2 is a flowchart of a process that may be implemented in someembodiments to characterize a collision using a trained classifier;

FIGS. 3A-3I illustrate examples of machine learning techniques withwhich some embodiments may operate;

FIG. 4 is a flowchart of a process that may be implemented in someembodiments to train a classifier;

FIGS. 5A-5E show values associated with an illustrative implementationof some techniques described herein; and

FIG. 6 is a block diagram of a computer system with which someembodiments may operate.

DETAILED DESCRIPTION

Described herein are various embodiments of a system that processesinformation describing movement of a vehicle at a time related to apotential collision to reliably determine whether a collision occurredand to determine one or more characteristics of the collision. In someembodiments, three-axis accelerometer data may be analyzed to identifyan indication of a potential collision between the vehicle and at leastone other object, such as one or more other vehicles or one or moreobstacles. In response to obtaining information regarding a potentialcollision, data describing movement of the vehicle before and/or after atime associated with the indication of the potential collision isanalyzed to determine whether the collision occurred and to determinethe characteristic(s) of the collision. In some embodiments, theanalysis of the vehicle movement data may be carried out at least inpart using a trained classifier that classifies the vehicle movementdata into one or more classes, where at least some the classes areassociated with whether a collision occurred and/or one or morecharacteristics of a collision. For example, classes may indicate, if acollision occurred, a severity of the collision and/or a direction ofimpact between the vehicle and the other object(s). If a collision isdetermined to be likely, one or more actions may be triggered based onthe characteristic(s) of the collision, such as automatically attemptingto contact a driver of the vehicle and/or automatically dispatchingemergency services.

Different collisions have different effects on the vehicle(s) and peopleinvolved in the collisions, and thus may justify different responses. Asevere collision may justify involving emergency services while a minorcollision may not. A collision in which a first vehicle is struck by asecond from the back may suggest fault lies primarily with the driver ofthe second vehicle, and different responses may be appropriate for theowner or operator of the first vehicle than for the second vehicle.Reliably determining, for a collision, whether a vehicle involved in thecollision is the first vehicle (that was struck) or the second (thatstruck the other vehicle) may therefore aid in determining anappropriate response to the collision.

Conventionally, accelerometer data has been used to determine whether avehicle has experienced a collision. Accelerometer data is used becauseaccelerometers are simple devices to use and also because of thewell-known relation between acceleration and force, and the conventionalunderstanding that when a vehicle experiences a suddenacceleration/force, this is a sign that the vehicle experienced acollision. In one such conventional approach, accelerometer data isanalyzed to derive an acceleration experienced by the accelerometer (andthus potentially by the vehicle) over time and to determine whether theacceleration at any time exceeds a threshold. If the accelerationexceeds the threshold, the vehicle is inferred to have experienced acollision.

The inventors have recognized and appreciated that such conventionalapproaches were unreliable and limited in the information they were ableto provide. Often, the conventional approach suffered from falsepositives (flagging a collision when there was not a collision) andfalse negatives (not flagging a collision when there was a collision).The inventors have recognized and appreciated that this lack ofreliability is because acceleration alone is not a reliable predictor ofwhether a collision has occurred. Vehicles or accelerometers may, undernormal circumstances, experience accelerations of similar magnitude toaccelerations experienced by a vehicle during a collision. For example,an accelerometer unit disposed in a passenger cabin of a vehicle may onoccasion be accidentally kicked or bumped, and it is difficult todifferentiate an acceleration associated with a kick or bump from anacceleration experienced during a collision. As another example, anaccelerometer unit disposed in a passenger cabin of a vehicle may beloosely mounted and may move while the vehicle is operating, such as bysnapping from one position to another when the vehicle is turning. Whenthe unit moves suddenly, this may be incorrectly flagged as a collision.As a further example, a large pothole or other road deformity may, whenstruck by a vehicle, cause a large acceleration value that may bedifficult to differentiate from acceleration values associated withcollisions. Kicks or bumps, and potholes, may therefore be flagged as acollision, a false positive. Similarly, during some collisions, avehicle may experience accelerations or forces that may be, totaled overtime, substantial, but that may be at any instant low as compared toaccelerations experienced during other collisions. Such a low value maybe similar to other accelerations that a vehicle naturally experiencesover the course of a normal drive. As a result, the accelerationsassociated with these collisions may be below a collision detectionthreshold to which the acceleration values are compared, resulting in afalse negative in which a collision is not detected.

The inventors recognized and appreciated that such techniques were alsolimited in the information they were able to provide. These conventionaltechniques provided binary indicators of whether a collision hadoccurred. They could not reliably provide additional informationcharacterizing the collision. Because of the unreliable nature ofacceleration analysis (discussed in the preceding paragraph) and thepoor link between accelerations experienced at any instant and thenature of the collision, severity information could not be reliablydetermined from comparison of acceleration data to thresholds. In somecases, conventionally, a maximum acceleration vector may be derived fromthe accelerometer data and this vector was equated to an estimate of adirection of impact in the collision. This technique, though, sufferedfrom the same lack of reliability as the underlying acceleration data onwhich it is based. Vehicles involved in collisions may move in a varietyof ways and directions. Simply analyzing an acceleration vector will notreliably indicate a direction of impact.

The inventors recognized and appreciated that machine learningtechniques may increase reliability of processing of accelerometer datato determine information about a potential collision. For example, aclassifier may be trained to classify accelerometer readings regarding apotential collision into whether a collision is or is not indicated, ora severity of the collision or other characteristics of the collision.

The inventors also recognized and appreciated, however, that while useof machine learning techniques may offer an increase in reliability, thereliability and amount of information about a collision that may bederivable solely from acceleration data from a time of a collision wouldstill be limited. Conventional approaches focused on acceleration datainferred to be related to an instant of a collision to determineinformation about collision, because of what was understood to be aclear link between acceleration of an object and a force acting on thatobject, and because force was assumed to be most informative of thenature of a collision. As discussed above, however, the inventors haverecognized and appreciated that acceleration data for a moment of acollision may not reliably characterize the collision.

The inventors thus recognized and appreciated that even if machinelearning were applied, if the analysis was based on acceleration datainferred to be for a moment of impact in a collision, the analysis wouldstill be of limited reliability. The inventors additionally recognizedand appreciated that if the analysis were to include other informationthat has not been conventionally recognized as informative as to thenature of a collision, this would increase the reliability and amount ofinformation that may be determined about a collision. Such otherinformation has not been conventionally collected or analyzed as part ofcollision analysis.

For example, the inventors recognized and appreciated that speedinformation for a vehicle, from a time surrounding a collision, may beinformative as to whether a collision has occurred. Often, it ispresumed that a vehicle simply decelerates and stops moving at a time ofa collision. The inventors recognized and appreciated that this is anincorrect presumption for many collisions. Often, a vehicle may continuetraveling for 3, 5, or 10 seconds following a collision. Over the timeof the collision and following the collision, acceleration values maynot necessarily indicate that the vehicle clearly experienced acollision because, as discussed above, the acceleration that the vehicleexperiences at any time during a collision may not be high. When avehicle's speed is reduced to 0, though, this may be a sign that thevehicle may have experienced an event, which may have been a collision.Together with this additional information that suggests a collision mayhave occurred, acceleration values from prior to the vehicle stoppingmay be analyzed for signs of a collision. Those signs, as should beappreciated from the foregoing, may not themselves clearly look like acollision, but when used together with speed information may moreclearly signal that it is likely that a collision occurred. For example,if other data indicates an event that may or may not be a collision atone time, and the vehicle's speed drops to 0 within a few secondsfollowing that suspect event, this may suggest that the suspect event ismore likely to be a collision.

The inventors further recognized and appreciated that reliablydetermining whether a collision has occurred and determining informationcharacterizing the collision may be enabled by monitoring movements of avehicle for a longer period of time surrounding a collision.Conventional approaches focused on an instant in time, which wasinferred to be the instant of first impact for a collision. Theinventors recognized and appreciated, however, that some collisions maylast quite some time, such as for more than a second or even up to 10seconds. Reviewing movement information for the entirety of thecollision may help in characterizing the collision. Beyond that, though,the inventors recognized and appreciated that by reviewing movements ofa vehicle from a time starting before a collision and lasting until atime following the collision, the reliability of determining whether acollision occurred and the reliability of characterizing the collisionmay be increased.

The inventors have thus recognized and appreciated that it may beadvantageous to try to determine a window of time that surrounds apotential collision, starting before the collision and ending after thecollision. Movement information for a vehicle in such a time period maythen be analyzed to determine whether the potential collision is likelyto have been a collision and, if so, to determine information about thecollision, such as severity information and/or direction of impact ofthe collision, or other information.

The inventors have thus recognized and appreciated that it may beadvantageous to determine an indication of a potential collision, fromwhich a time period may be generated and movement information for thetime period collected for analysis. While acceleration data has limitedreliability, as discussed above, for use as a sole factor in collisionanalysis, if analyzed in a particular manner it may be useful as anindicator of a potential collision, for use in determining the timeperiod. For example, while each of the three acceleration valuesgenerated by a three-axis accelerometer, each of which indicates anacceleration in one of three directions (forward-backward, right-left,and up-down) may be of limited use in some cases in indicatingoccurrence of a collision, together these values may be more indicativeof potential collision. Taken together, the three values may be used toproduce a scalar value that indicates a total acceleration of a vehicleat an instant. This total acceleration value, if above a threshold, maybe indicative of a sudden event that the vehicle experienced that causedthe vehicle to suddenly accelerate. This may be a collision or, ofcourse (due to the limited information available from acceleration data)a pothole or other road deformity. But, this may be sufficientlyinformative of a potential collision to define a time period from beforeand after the instant associated with that acceleration, which may befurther analyzed to determine whether a collision occurred and tocharacterize the collision. Such an analysis may, as discussed above, beperformed at least in part using a trained classifier.

The inventors have further recognized and appreciated that otherinformation describing a vehicle involved in a potential collision mayalso aid in determining whether a collision occurred and/or incharacterizing the collision. For example, data generated by thevehicle's Engine Control Unit (ECU) and/or otherwise available from orvia the vehicle's On-Board Diagnostics (OBD) system (which, as usedherein, also refers to an OBD-II system) may aid in determining whethera collision occurred. For example, information indicating whether any ofthe vehicle's airbags were deployed may be a strong indicator that acollision occurred. Using this as a strong sign that a collisionoccurred, movement information (e.g., accelerometer and/or speed data)might be analyzed characterize the collision, such as the severity ofthe collision and/or a direction of impact. As another example, if anyof a vehicle's sensors indicated a fault at a time of a potentialcollision, this may be a sign of damage associated with a collision andindicative that the potential collision was a collision. Movementinformation may then be analyzed to characterize the collision.

In view of the foregoing, techniques are described herein for monitoringmovement information for a vehicle to identify an indication of apotential collision. The indication of the potential collision may be,for example, a determination that a scalar value derived from three-axisaccelerometer data indicates a magnitude of total acceleration at apoint in time that is above a threshold value. Rather than adetermination from acceleration data being taken as conclusive evidenceof a collision, as in conventional approaches, and rather thaninformation about this point in time driving the analysis, this point intime is used in some embodiments as a point from which to define a timeperiod, and for obtaining for the time period additional movement datafor the vehicle that indicates movements of the vehicle before and aftera time associated with the indication of a potential collision, and/orfor obtaining data from the vehicle that indicates a status of one ormore systems or components of the vehicle during the period of time.

The movement data that is obtained before and after the time mayinclude, for example, multi-axis (e.g., three-axis) accelerometer datafor multiple points over a time period that extends before and after thetime. The movement data may additionally include a scalar value for eachpoint that indicates a magnitude of total acceleration experienced bythe vehicle (or by the accelerometer) at that point. In addition,movement information may include speed of the vehicle at each point.This movement information, including for each point the three-axisaccelerometer data indicating an acceleration in each of threedirections, a magnitude of total acceleration, and/or a speed, may beanalyzed with a trained classifier to determine whether the indicationof a potential collision is associated with a likely collision. Thetrained classifier may additionally or alternatively be used to analyzethe vehicle movement information to determine one or morecharacteristics for such a collision. Such characteristics may include aseverity of the collision and/or a direction of impact of the collision.

In some embodiments, if a collision is determined to be likely, one ormore actions may be triggered based on the characteristic(s) of thecollision, such as automatically attempting to contact a driver of thevehicle and/or automatically dispatching emergency services.

Described below are illustrative embodiments of approaches for obtainingand analyzing vehicle information to reliably determine whether avehicle has experienced a collision and/or one or more characteristicsof such a collision. It should be appreciated, however, that theembodiments described below are merely exemplary and that otherembodiments are not limited to operating in accordance with theembodiments described below.

FIG. 1 illustrates a computer system with which some embodiments mayoperate. FIG. 1 includes an organization 100 that may operate a fleet ofvehicles 102. The organization 100 may be a commercial enterprise, agovernment or government agency, a not-for-profit organization, or anon-profit organization, or any other organization. Embodiments are notlimited to operating with any particular form of organization, or withformal or informal organizations. Illustrative examples of such anorganization include a commercial service that delivers goods and/orservices to customers' homes or businesses, a business that rentsvehicles, a municipality that operates vehicles within the municipality(e.g., vehicles to perform public works projects, public safety vehicleslike police cars, fire trucks, and ambulances, etc.). The vehicles ofthe fleet 102 may be operated by employees and/or contractors of theorganization 100, or by others (e.g., customers of a rental car agencymay drive the cars).

The organization 100 may want to be notified promptly when any of thevehicles 102 are involved in a collision. The organization 100 may wishto respond to such a collision by determining whether the driver (e.g.,the employee or contractor) or any other person was injured. Theorganization 100 may also wish to respond to a collision by determiningwhether the vehicle is still safe to operate, or has been damaged to thepoint that it should not be operated and another vehicle should be sentto act in the place of the damaged vehicle (e.g., by taking ondeliveries that the damaged vehicle was to have made, or otherwiseproviding service the damaged vehicle was to be operated to perform).Such information might be inferred or determined from an indication of aseverity of a collision. More severe collisions may be more likely thanless severe collisions to result in injuries or result in vehicles thatcan no longer be safely operated. Accordingly, if severity of acollision could be determined, the organization 100 may also be able toestimate whether anyone was injured or whether the vehicle can still besafely operated.

The organization 100 may also want to know, when a collision hasoccurred, the likelihood that it will incur liability for the collision.Fault for different collisions falls with different parties, and thefault may be inferred from a manner in which two vehicles collided. Theangle at which a vehicle in the fleet 102 struck or was struck byanother object (e.g., another vehicle or obstacle) may thus beindicative of who is at fault for the collision, and may be indicativeof whether the organization 100 will incur liability. For example, if avehicle in the fleet 102 is hit from behind by another vehicle, it maybe less likely that the driver of the vehicle in the fleet 102 is atfault and less likely that the organization 102 will incur liability. Ifthe vehicle in the fleet 102 hits another vehicle with its front end,though, it may be more likely the driver of the vehicle in the fleet 102is at fault and more likely that the organization 102 will incurliability. Accordingly, if angle of impact information can be determinedfor a vehicle involved in a collision, the organization 100 may be moreeffectively able to determine who may be at fault and whether it islikely to incur liability.

FIG. 1 also illustrates a collision between two vehicles 104, 106, inwhich vehicle 104 is being struck from behind by vehicle 106. In thisexample, vehicle 104 is a member of the fleet 102. Techniques describedherein may be used to obtain movement information for vehicle 104 thatmay be analyzed to determine whether a collision occurred and tocharacterize the collision, including by determining a severity of thecollision and/or angle of impact on vehicle 104.

In some embodiments, each of the vehicles 104, 106 may be respectivelyequipped with a monitoring device 104A, 106A. The monitoring device104A, 106A may include a three-axis accelerometer that indicatesacceleration of the device over time, which may be indicative ofacceleration of the associated vehicle over time. The device 104A, 106Amay be equipped to produce an accelerometer value at a set interval,such as multiple times per second (e.g., 100 times per second), once persecond, or at another suitable interval. In some embodiments, themonitoring devices 104A, 106A may also be equipped to obtain informationfrom one of the associated vehicles. For example, a monitoring device104A, 106A may be equipped to connect to an OBD port of an associatedvehicle and obtain information from an ECU or OBD system of the vehicle.Such information may include fault messages generated by the ECU or OBDsystem, or messages indicating a state of components of the vehicle,such as messages indicating whether an air bag has deployed.

A collision detection facility may be implemented as executableinstructions and may analyze information generated or obtained by amonitoring device 104A, 106A. The collision detection facility mayanalyze the information to determine whether a vehicle associated withthe monitoring device 104A, 106A has experienced a collision and, if so,determine one or more characteristics of the collision (e.g., severity,angle of impact).

In some embodiments, the collision detection facility may be implementedin (e.g., stored by and executed by) the monitoring device 104A, to makesuch determinations about vehicle 104. In other embodiments, thecollision detection facility may be implemented by another device of thevehicle 104, such as a computing device integrated with the vehicle 104(e.g., the ECU, or a computer of the OBD system), or a computing devicedisposed in a passenger cabin of the vehicle 104. Such a computingdevice disposed in the passenger cabin may be a mobile device (e.g.,smart phone, tablet, etc.) or personal computer (e.g., laptop computer),or other suitable device. In other embodiments, the collision detectionfacility may be implemented remote from the vehicle 104. In theembodiment of FIG. 1, for example, the collision detection facility maybe implemented in one or more servers 108 located remote from thevehicle 104. For example, the server 108 may be one or more serversoperated by a vendor of the monitoring device 104A, one or more serversoperated by the organization 100, operated by a cloud computingplatform, or other servers.

In still other embodiments, operations of the collision detectionfacility described herein may not be implemented wholly in one locationor another, but may be split in any suitable manner. As one suchexample, operations of a collision detection facility to determinewhether a collision has occurred may be implemented within themonitoring device 104A or otherwise local to the vehicle 104, whereasoperations to characterize a collision, once it is determined that acollision is likely to have occurred, may be implemented remote from thevehicle 104 in the server(s) 108.

Regardless of where it is implemented, in accordance with sometechniques described herein, the collision detection facility of theexample of FIG. 1 may make use of a trained classifier to determinewhether a collision has occurred and/or to characterize the collision.The trained classifier may have information associated with each of theclasses with which it is configured, illustrated in FIG. 1 as data store108A. That information may be used by the trained classifier to analyzeinformation about the vehicle 104 obtained by the monitoring device104A, including movement data or other data, and determine a class thatbest matches the obtained data.

Each of the classes may be associated with whether or not a collisionhas occurred and/or, if a collision has occurred, one or morecharacteristics associated with the collision. For example, classes maybe associated with a binary decision of whether a collision occurred ordid not occur. As another example, classes may be associated withdifferent levels of likelihood that a collision occurred. As a furtherexample, classes may be additionally or alternatively associated withone or more characteristics of a collision, such as a severity of acollision, different levels of severity of a collision, different anglesof impact, or other characteristics of a collision.

Additional information regarding examples of use of a trained classifieris provided below in connection with FIGS. 2-5E.

In embodiments in which the collision detection facility is implementedremote from the monitoring device 104A, the monitoring device 104A maycommunicate obtained data to the collision detection facility. Themonitoring device 104A may include communication components, such as oneor more wireless transceivers. The wireless transceiver(s) may include,for example, components for communicating via a Wireless Wide AreaNetwork (WWAN), such as via a cellular protocol such as the GeneralPacket Radio Service (GPRS), Universal Mobile Telecommunications Service(UMTS), Enhanced Data Rates for GSM Evolution (EDGE), Long-TermEvolution (LTE), or other suitable protocol. In some such embodiments,the monitoring device 104A may directly communicate with one or morenetworks outside the vehicle 104 to communicate data to the collisiondetection facility. In other embodiments, the monitoring device 104A maycommunicate to such networks via another device disposed local to thevehicle 104. For example, the vehicle 104 may include communicationcomponents for communicating via a WWAN and the monitoring device 104Amay communicate to the vehicle 104 to request that data obtained by themonitoring device 104A be sent to the collision detection facility. Asan example of such an embodiment, the monitoring device 104A may includecomponents to communicate via a Controller Area Network (CAN) of thevehicle 104 and request that obtained data be transmitted from thevehicle 104. In still other embodiments, the monitoring device 104A maycommunicate via a mobile device local to the vehicle 104, such as amobile device operated by a driver of the vehicle 104. The mobile devicemay be, for example, a smart phone or tablet computer. In such anembodiment, the monitoring device 104A may communicate with the mobiledevice via a Wireless Local Area Network (WLAN) or Wireless PersonalArea Network (WPAN), such as any of the IEEE 802.11 protocols or any ofthe Bluetooth® protocols, to request that obtained data be sent to thecollision detection facility.

Together with obtained data describing movements of the vehicle or otherinformation, the monitoring device 104A may transmit to the collisiondetection facility one or more identifiers for the vehicle 104 and/orfor the monitoring device 104A, to indicate that the transmitted datarelates to the vehicle 104. Embodiments are not limited to operatingwith a particular form of identifier. In some embodiments, a VehicleIdentification Number (VIN), a license plate number, or other identifiermay be used. The collision detection facility may receive thisinformation from a monitoring device, which may be configured with thisinformation, such as by receiving the information as input when themonitoring device is installed in the vehicle 104. The collisiondetection facility may also receive this information when the collisiondetection facility is executing on a computing device integrated withthe vehicle 104 (in which case the facility may obtain the identifierfrom memory), or when the facility receives data from or via the vehicle104, in which case one or more components of the vehicle may add theidentifier to the information that is sent.

In some embodiments, an identifier or contact information for a driverof the vehicle 104 may be obtained and transmitted. For example, a phonenumber that may be used to contact the driver of the vehicle 104 may besent. This may be sent in embodiments in which the collision detectionfacility is executing or, or receives data from or via, a mobile deviceof the driver, in which case the mobile device may send data from themonitoring device 104A together with the phone number. In otherembodiments, a driver of the vehicle 104 may “log in” to the monitoringdevice 104A or otherwise configure the monitoring device 104A when firstoperating the vehicle 104, and as part of that configuration may providean identifier and/or phone number for the driver.

In some embodiments, location data for the vehicle 104 may also be sentto the collision detection facility. For example, the monitoring device104A may include Global Positioning System (GPS) hardware to determine alocation of the monitoring device 104A, or the monitoring device 104Amay obtain from vehicle 104 information describing a location of thevehicle 104. The monitoring device 104 may also transmit this locationinformation to the collision detection facility.

The collision detection facility, upon analyzing the data anddetermining one or more classes that likely describe the suspectcollision, may report the suspect collision to the organization 100. Forexample, the collision detection facility may communicate to one or moreservers 110 associated with the organization 100. The server(s) 100 maybe associated with a call center or other employee or group of employeestasked with reviewing and potentially responding to collisions orpotential collisions. The server 100 may thus be operated by theorganization 100 and/or by a service provider that the organization 100has engaged to monitor and respond to collisions or potentialcollisions.

The collision detection facility may provide various information to theorganization 100 when reporting a collision or potential collision. Forexample, if the collision detection facility determines one or morecharacteristics of the potential collision, such as a severity and/orangle of impact for the collision, the characteristic(s) may be sent tothe organization 100. In some cases, some of the data obtained by themonitoring device 104A and sent to the collision detection facility maybe sent. For example, if data was obtained from the vehicle 104, such asinformation indicating whether an air bag was deployed, this informationmay be sent to the organization 100. The identifier for the vehicle 104and/or the monitoring device 104A may be transmitted, so theorganization 100 can identify a vehicle to which the report relates. Inembodiments in which the collision detection facility receives anidentifier or contact information for a driver, the identifier orcontact information may also be sent. In embodiments in which locationinformation for the vehicle 104 is received by the collision detectionfacility, the location information may also be sent to the organization100.

Upon receipt of a report of a collision or potential collision at theorganization 100, the organization 100 may determine whether and how torespond. The response of the organization 100 may be manual and/orautomatic, as embodiments are not limited in this respect. Inembodiments in which the response of the organization 100 is at leastpartially automatic, the automatic response may be generated using rulesthat evaluate information received from the collision detectionfacility. For example, if a report from a collision detection facilityindicates that the vehicle 104 is likely to have experienced a severecollision, and the report includes location information, thisinformation may satisfy conditions associated with triggering dispatchof emergency services to the location, and the server(s) 110 may triggerthat dispatch without human intervention, such as by sending locationinformation and/or identifying information to the dispatcher. In othercases, though, a person may review the report from the collisiondetection facility and determine how to respond. The person may respondby attempting to contact a driver of vehicle 104, such as using receivedcontact information for the driver, to inquire as to health or safety ofthe driver or others. The person may also contact emergency servicesand/or roadside assistance services in an area in which the vehicle 104is located, to request dispatch of emergency services or roadsideassistance to the vehicle 104 using the location information and/oridentifying or contact information.

Automatically and/or manually carrying out these or other responses may,in some embodiments, include communicating with one or more computingdevices 112 associated with one or more service providers, such asemergency services or roadside services.

Communications in the computer system of FIG. 1 may be carried out usingone or more wireless and/or wired networks, including the Internet,generally depicted in FIG. 1 as communication network(s) 114. It shouldbe appreciated that the communication network(s) may include anysuitable combination of networks operating with any suitablecommunication media, as embodiments are not limited in this respect.

FIG. 1 illustrated examples of components of a computer system withwhich some embodiments may operate. Described below in connection withFIGS. 2-5E are examples of implementations of a collision detectionfacility, including techniques for training a collision detectionfacility. These embodiments may operate with a computer system like theone shown in FIG. 1, or with another form of computer system

FIG. 2 illustrates an example of a process that may be implemented by acollision detection facility in some embodiments. The process of FIG. 2may be implemented local to a vehicle (e.g., in a monitoring device ofFIG. 1) and/or remote from a vehicle. In some embodiments, for example,part of the process 200 of FIG. 2 may be implemented local to a vehicle,such as operations of blocks 202-206 of the process 200, while anotherpart of the process 200 may be implemented from a vehicle, such asoperations of blocks 208-212.

The process 200 begins in block 202, in which a collision detectionfacility obtaining information regarding a potential collision.

In some embodiments, the collision detection facility may obtaininformation regarding a potential collision by monitoring over time amagnitude of total acceleration experienced by an accelerometer of avehicle and/or of a monitoring device. The total acceleration may be avalue derived from acceleration detected by the accelerometer indifferent axes. For example, in a case that the accelerometer is athree-axis accelerometer, the total acceleration may be derived fromcomputation performed on acceleration experienced in each of the threeaxes. The three axes may be forward-backward, right-left, and up-down insome cases. In some embodiments, the magnitude of total acceleration maybe calculated as the square root of the sum of the squares of theacceleration in different direction. For example, if the acceleration inthe forward-backward direction is assigned to “x”, the acceleration inthe right-left direction to “y,” and the acceleration in the up-downdirection to “z,” the magnitude of the total acceleration may be:

acc _(total)=√{square root over (x ² +y ² +z ²)}

The magnitude of the total acceleration is a scalar value. This valuemay be used in block 202 as part of obtaining information on whether thevehicle has experienced an event that may (or may not be) a collision—apotential collision.

For example, in some embodiments, if the vehicle experiences a totalacceleration at a time that is above a threshold, this may be taken as asign of a suspected collision that is to be further evaluated todetermine whether it is a collision or is not a collision. As should beappreciated from the foregoing, acceleration alone is seldom a reliableindicator of a collision, as other events may also be associated withhigh accelerations, such as a kick or bump of a monitoring device or thevehicle striking a pothole or other road deformity. Accordingly, themagnitude of total acceleration is not taken as a sign of a collision,but rather used as a sign of a potential collision that is to beinvestigated further.

Thus, in block 202 in some embodiments, the collision detection facilitydetermines the total acceleration over time, such as at a time interval.That time interval may be, for example, multiple times per second (e.g.,100 times per second), once per second, or other suitable interval, thendetermines whether the total acceleration at any time exceeds athreshold. If not, the collision detection process ends. In someembodiments, the collision detection facility may return to block 202and continue monitoring the total acceleration over time, or otherwiseobtaining information on a potential collision.

It should be appreciated, however, that embodiments are not limited tousing a threshold to determine whether a potential collision hasoccurred, as embodiments may obtain information regarding a potentialcollision in other ways.

For example, in some other embodiments, an indication of a potentialcollision may be obtained by identifying a total acceleration that is alargest in a time period. When a magnitude of total acceleration at atime exceeds the magnitude of total acceleration of other times, such asother times within a time window surrounding a time being analyzed, thathigher total acceleration may be taken as an indication of a potentialcollision. This may be the case even if the magnitude of acceleration atthat time is lower than the magnitude at other times. In such a case,the collision detection facility may use a sliding time window to, overtime, analyze acceleration data within the time window to determinemagnitude of total acceleration at times within the time window and todetermine the highest magnitude in the window. The time of that highestmagnitude may then be taken as a time of a potential collision and takenin block 202 as information regarding a potential collision.

In some other embodiments, rather than the collision detection facilityusing a sliding time window to identify a maximum total accelerationwithin the time window, a sliding time window may be used thatdetermines every successive point in time to be an indication of apotential collision. At each time step, a next acceleration sample maybe taken as an indication of a potential collision, and taken in block202 as information regarding a potential collision.

Once the collision detection facility obtains information regarding apotential collision in block 202, then in block 204 the collisiondetection facility defines a time period that spans a time before andafter the time at which the total acceleration exceeded the threshold.The time may be long enough to last before and after a collision, if thetime of the potential collision is at the beginning, during, or at theend of a collision. For example, if collisions are determined to last atleast three seconds, the time period may be 6 seconds long: threeseconds before the time of the potential collision, and three secondsafter. If collisions are determined to last at least 5 seconds, the timeperiod may be 10 seconds. The inventors recognized and appreciated thatsome collisions may last up to 10 seconds, so a time period of 20seconds may be advantageous. It should be appreciated, though, thatembodiments are not limited to being implemented with any particulartime period. Further, while in some embodiments the time period may besymmetrically defined around the time from block 204, in otherembodiments the time period may be asymmetrically defined.

In block 206, the collision detection facility obtains data describingthe vehicle during the time period defined in block 204. The data thatis obtained may be movement data describing movements of the vehicle inthe time period. The data describing the movements may be accelerationdata indicating an acceleration of the vehicle in three axes atintervals (e.g., the same interval that may be used in block 202 toobtain acceleration data) during the time period. In some embodiments,the acceleration data may also be processed to determine additionalinformation describing movements of the vehicle in the time period. Forexample, for each set of acceleration data for each interval, amagnitude of total acceleration may be determined, in the same mannerthat may have been used, in some embodiments, in block 202. As anotherexample, speed of the vehicle at the interval may be determined. In someembodiments, speed information may be determined from calculationsperformed on accelerations over time, potentially together with locationdata.

In some embodiments, in addition to or as an alternative to accelerationinformation, other data describing the vehicle may be obtained. Forexample, the collision detection facility may obtain data from avehicle, such as from an ECU or OBD system of the vehicle. The obtainedinformation may include, for example, speed at each of the times forwhich acceleration data was obtained. The obtained information mayadditionally or alternatively include messages generated by one or morecomponents of the vehicle, such as information indicating a state of thecomponent(s). The state information may include, for example, whetherany of the components of the vehicle have generated a fault messageand/or have changed a state, such as, for an air bag system, whether anair bag deployed. The data that is obtained from the vehicle may beinformation for the time period defined in block 204.

In block 208, the information that is obtained in block 206 is analyzedwith a trained classifier of the collision detection facility. Asdiscussed above in connection with FIG. 1, the trained classifier mayinclude multiple different classes that are associated with vehicle datafor different scenarios, where the scenarios include whether a collisionoccurred and/or characteristics of collisions (e.g., severity, angle ofimpact, etc.).

Each class may be associated with data describing combinations of data(e.g., movement data or other data) that are associated with thescenario described by the class. For example, if a class is associatedwith a collision having occurred and with a collision that is a severecollision in which the vehicle was rear-ended by another, the class maybe associated with characteristics of movement data and/or other datathat define such a severe rear-end collision. This information maydefine the class and be used to determine whether new data (for apotential collision to be analyzed) fits any of the classes.

Each class may be defined by different movement data because each typeof collision may be associated with different movements, which allowsfor differentiating collisions, and because normal vehicle operations(with no collision) may also be associated with movements that differfrom movements associated with collisions, which allows fordifferentiating collisions from normal driving. For example, astraightforward rear-end collision may include movements that areprimarily forward-backward movements. A collision in which the vehicleis struck broadside by another vehicle may, on the other hand, beassociated with right-left movement data. If data is input for asuspected collision, and that data includes primarily forward-backwardmovements and includes very little right-left movement, it may be morelikely that the suspected collision is a rear-end collision than thatthe suspected collision is a broadside collision. Severe collisions mayalso demonstrate different movements than not-severe collisions, andnot-collisions may demonstrate different movements than collisions. Acomparison of data for a suspected collision to data describingdifferent classes of collisions may therefore allow for determiningwhether a collision occurred and/or for determining one or morecharacteristics of a collision.

Accordingly, when the data obtained by monitoring device 104A isanalyzed with the trained classifier, the movement data and/or otherdata may be compared to each of the classes defined by the trainedclassifier. The collision detection facility may generate a probabilityindicating a level of match between each of one or more classes and thedata. The probability for a class indicates a likelihood that theinformation associated with that class is an accurate description of thedata. For example, for the example class above that is associated with acollision having occurred that is a severe rear-end collision, the inputdata may be compared to determine whether it matches the data for thatclass. If the collision actually was a severe rear-end collision, theinput data may appear similar to the data for the class and a highprobability of match may be generated. If, however, the input data isassociated with a not-severe front-end collision, there may not be ahigh degree of match to the severe rear-end collision class and,accordingly, a low probability of match may be generated by thecollision detection facility. In such a case, though, the trainedclassifier may have another class for a not-severe front-end collision,and there may be a high probability of match to that class. As a result,the collision detection facility may generate a probability of matchbetween the input data and each of one or more classes maintained by thetrained classifier.

These probabilities may be used to determine whether a collisionoccurred and/or characteristics of the collision. For example, a classthat has a highest probability may be selected as the accurate answer,and the collision information for that class (whether a collisionoccurred and/or characteristics of such a collision) may be chosen asthe likely correct descriptor of the collision. As another example, theprobabilities may be compared to a threshold to determine whether any ofthe probabilities for any of the classes are above the threshold. If so,all of the classes for which a probability are above a threshold may bereported as potential matches for the suspected collision, for a user toreview each of the potential matches and the collision information foreach of the potential matches. As another example, the probabilities maybe compared to determine whether one or more of the probabilities differfrom others by more than a threshold amount, such that one or more couldbe determined to be potential correct matches whereas others are, ascompared to those one or more, less likely to be correct. Those one ormore that stand out from the others may then be reported as potentialmatches for the suspected collision, for a user to review each of thepotential matches and the collision information for each of thepotential matches. Embodiments are not limited to any particular mannerof analyzing probabilities and selecting one or more potential correctmatches from the probabilities.

Accordingly, by comparing the data obtained in block 206 to the datadefining the different classes/scenarios, the trained classifier candetermine which of the classes is a likely match or best match for thedata obtained in block 206. The collision detection facility maytherefore, in block 208, determine one or more classes that are a likelyor best match to the data obtained in block 206.

In block 210, based on the class(es) determined in block 208, thecollision detection facility determines whether the potential collisionfrom block 202 is likely to be or have been a collision. This mayinclude determining whether the class that best matches the dataobtained in block 206 is a class associated with a collision, or whetherany of the classes to which the obtained data is a good match is a classassociated with a collision. This may alternatively include determiningwhether a probability of match to any class associated with a collisionexceeds a threshold, or whether a probability of match to any classassociated with no collision exceeds a threshold.

If it is determined in block 210 that a collision is not likely, then inthe embodiment of FIG. 2 the collision detection process may end, orreturn to block 202 and continue monitoring acceleration over time orotherwise obtaining information regarding another potential collision.

If, however, the collision detection facility determines in block 210that a collision is likely to have occurred, then in block 212 thecollision detection facility triggers actions responding to thecollision. This may include notifying an operator of a fleet of vehiclesof which the vehicle is a member, notifying roadside assistance,notifying emergency services, attempting to contact a driver of thevehicle, or other actions discussed above. Once the actions aretriggered, the collision detection facility may end the process 200, orcontinue monitoring in block 202, with either monitoring the vehiclethat experienced the collision or monitoring other vehicles.

In some embodiments, the collision detection facility may evaluate aclass identified as the most likely match for a suspected collision forwhich data was received and analyzed by the collision detectionfacility. If the best match determined by the classifier indicates thata collision is unlikely to have occurred, the collision detectionfacility may not report the potential collision to the organization 100.If, however, the collision detection facility determines that acollision may have occurred, the facility may report the potentialcollision to the organization 100. In other embodiments, however, thecollision detection facility may report to the organization 100 everypotential collision it analyzes, but may report the potential collisionto the organization 100 together with a value indicating a probabilitythat the potential collision was a collision. A person at theorganization 100 (or a vendor for the organization 100) reviewing thereport may then analyze the likelihood that the potential collision wasa collision and, based on the probability, determine whether and how torespond.

In one example described above of the implementation of block 202, thetime period is defined in block 204 to be a time period surrounding atime at which a total acceleration exceeded a threshold, which is oneexample of a way in which information regarding a potential collisionmay be obtained. In some embodiments, the time at which a totalacceleration exceeds a threshold may trigger an analysis of a timeperiod before and after that time, to identify a maximum totalacceleration in the time period. This time period may be the same ordifferent than the length of the time period of block 204. In some suchembodiments, once the maximum total acceleration in the time period isdetermined, the time period of block 204 is defined based on a timeassociated with that maximum total acceleration, and data is obtained inblock 206 for that time period.

The collision detection facility was described in connection with theexamples of FIGS. 1 and 2 as implementing machined learning techniques,using a trained classifier. It should be appreciated that embodimentsare not limited to implementing the trained classifier or the machinelearning techniques in any particular manner.

In some embodiments, the machine learning may be implemented using ak-Nearest Neighbor technique. k-Nearest Neighbor (short k-NN) is anexample of instance-based learning. This means that the training data isbeing stored for comparison purposes. New data will be classified bytaking a defined number of the closest training data into consideration.The k-NN algorithm is explained in the following example shown in FIG.3A. The goal is to determine a classification of the “New” data point byconsidering how similar it is to its neighbors in the graph. The “New”data point is positioned in the graph at coordinates determined by oneor more data values associated with the “New” data point. The “k” in thek-Nearest Neighbor algorithm refers to how many other data points arechosen for evaluation. Points are chosen for having a closest lineardistance to the “New” data point in the graph. FIG. 3A shows twodifferent options, one with k=3 and one with k=6. When three neighborsare considered, it is seen that two of the three are class “A” whileonly one is class “B,” and thus the “New” data point will be determinedto be a member of class “A.” On the other hand, in the example where sixneighbors are considered, only two of the six are class “A” while theother four are class “B.” As such, for the k=6 example, class “B” willbe chosen for the “New” data point. k-NN is a good choice for a trainedclassifier for a small data set with few dimensions, because of its highreliability and computational simplicity in these situations.

In other embodiments, a Random Forest (RF) technique may be used. RFbelongs to the machine learning category called “decision trees” and canbe applied to classification tasks. Well-advanced trees which are thefoundation of the RF. An RF model is trained by creating a decision treethat can represent most of the training data, by creating paths throughthe tree to labels reflected in the training data. The tree will then beevaluated for new input data and will output a predicted label at theend of the path. FIG. 3B shows a small decision tree with four branchesfor classifying specific flowers based on the sepal length and width ofthe flowers.

One advantage of decision trees is the ease of understanding the model.The predictor space is segmented in a number of simple regions which canbe defined by splitting rules. Splitting rules are the basic element ofdecision trees. One drawback of decision trees, however, is apotentially poor accuracy and a high risk of overfitting the decisiontree to the training data. “Overfitting” may occur when a very detailedtree is created with hundreds of nodes that works perfectly on thetraining data, but has poor results when applied to data not in thetraining set.

One modification of a standard decision tree algorithm is called“Bagging.” This method uses, instead of one decision tree, multipledecision trees. In some cases, hundreds of independent decision treesmay be constructed by using a bootstrap sample of the dataset. Toclassify new input data, the data is processed using all or multiple ofthe trees and a majority vote is taken over the generated predictions.Bagging can be used for regression and classification tasks.

By adding even more randomness to Bagging, a Random Forest algorithm isimplemented. In RF trees, each tree or each node is randomly takingseveral features of input data into consideration. The random featureselection creates independency around the trees compared to regularBagging. Most times the algorithm obtains better results than Bagging,because of better variance and bias tradeoffs. Extremely randomizedtrees take this even further.

In other embodiments, the trained classifier may be advantageouslydefined using a neural network, such as a convolutional neural network(CNN). The inventors have recognized and appreciated that in some cases,a CNN may provide for higher reliability and accuracy than other machinelearning techniques.

Neural networks are implemented as mathematical models that are viewedas something of a metaphor for or simulation of functions of neurons ofan organic brain. Some neurons inside of a brain perform a simple taskequivalent to outputting an electric signal when the input into theneuron exceeds a predetermined threshold. Warren McCulloch and WalterPitts designed the first computational model in 1943 that simulated aneuron with the help of mathematics and a threshold logic for theactivation. The basic structure of a neuron is displayed in the FIG. 3C.The inputs x₁, x₂, . . . , x_(n) are multiplied with weights named w₁,w₂, . . . , w_(n). These are then added together with a bias node calledb. This value is passed through an activation function. The type offunction chosen depends on the use case and implemented layer. A reasonto add a bias is for example to shift the activation function, in whichcase only higher x values would produce an output. The output can berepresented as a value z with the following formula:

$z = {b + {\sum\limits_{i = 1}^{n}{w_{i}x_{i}}}}$

Neural network have multiple layers of connected neurons, whichrepresent complex nonlinear relationships between inputs and outputs. Anexample structure of a neural network is displayed in FIG. 3D. Thearchitecture of a fully connected neural network consists of an inputlayer, hidden layer and output layer. The number of neurons in eachlayer can be adjusted. Feed-forward neural networks are the simplest ofits kind with information moving only in one direction. The movementstarts from the input nodes, goes through the hidden layers and ends inthe output nodes. By implementing multiple hidden layers the NNs aremost common referred to as Deep Neural Networks (DNN). The example ofFIG. 3D is a DNN.

The realization of such complex models is possible through strongcomputation power and a sufficient amount of data. Adjusting theparameters of the network influences the activation logic behind eachneuron. While the network is being trained, the weights of each neuronare adjusted to meet a desired representation of the provided dataset.One training technique, called Gradient Descent (GD), works by finding aminimum of a cost function in an iterative manner. A learning rate isdefined before and specifies the size each step will take to theminimum. This means that a large learning rate might end up causing abouncing optimization while, on the other hand, a very small learningrate might take a long time to arrive at the desired representation.During learning, a change in the cost function is analyzed at each step.Once the cost function is not decreasing (or not decreasingsubstantially) and/or remains on the same level, it is determined thatthe problem has converged and the NN is trained.

Training Neural Networks can result in perfect representations of theprovided training datasets which means bad accuracies for the testdataset. One possibility to prevent Neural Networks from overfitting iscalled dropout. This technique drops a defined number of neurons insideof the network. Dropout can be implemented at the last layer of thenetwork or between every layer for example.

Another way of improving the network is to change activation functions.Each neuron can be influenced by different types of activationfunctions. One known example is the sigmoid function. The function'soutput is always between 0 and 1. The mathematical representation is thefollowing:

${f(t)}{= \frac{1}{1 + e^{- t}}}$

In other layers it might be more advantageous to gain only values whichare between −1 and 1. This activation function can be realized by thehyperbolic function tanh. It is represented by the following formula:

${\tanh (x)} = {\frac{2}{1 + e^{{- 2}x}} - 1}$

Saturating functions can result in optimization problems, as thegradient equals 0 at large and small x values. Non-saturated activationfunctions turn out to solve an exploding or vanishing gradient andaccelerate the convergence speed. The Rectified Linear Unit, short ReLU,is another activation function. Its output remains 0 for all negativevalues and does not change for positive values. The ReLU function isrepresented by the following mathematical formula:

f(x)=max(0, x)

The early successes to solve new complex tasks cleared the way todevelop new categories of Neural Networks. Compared to early NeuralNetworks, models available in 2018 are far more advanced and cansubstitute more and more work done by humans.

When time-series data is to be input to a Neural Network (such as insome embodiments described herein), this may present complexities inNeural Networks. Training simple NNs on time series data would beinefficient, because the network would adjust its parameters on valuesgiven to particular time steps. Instead it is more efficient for thenetwork to look for patterns in the structure of the data represented.Taking this into consideration, one specific type of Neural Network maybe an advantageous option in some embodiments.

Convolutional Neural Networks (CNNs) are a subclass of Neural Networks.With the amount of collected data in some scenarios (e.g., for imagerecognition), a Neural Network can get very complex and hard to train.CNNs can produce very close accuracies and use less connections as wellas parameters. This makes them very powerful and easier to train.

The main difference between a CNN and a standard NN is that the sumpresented in the neural formula for the NN is substituted by aconvolutional operation. These layers are called convolutional layers.Furthermore, CNNs often have layers that reduce the resolution of thedata, referred to as pooling layers. The architecture of the network isseparated into at least convolutional layers, pooling layers, and anoutput layer.

Convolutional layers determine which inputs are fed into each neuron,potentially with the help of filters applying filter parameters. Thesefilters can be constructed using different sizes, defined by a kernelvariable. The purpose of a filter is to evaluate data and multiply eachfilter parameter with a respective value in the input, which may beconsidered akin to element-by-element multiplication of matrices.

The filter will typically create exactly one output for each portion ofinput data. The manner in which the filter evaluates the input data canbe defined by changing the “stride value.” A stride of 1 would mean thefilter moves one data value at a time. The result of all operationscreates new data having a same data size as the input data. One examplefilter with the dimensions 3×3 is shown in FIG. 3E. The parametersinside the filters are randomly chosen before training the network.

FIG. 3F represents an input data having dimensions of 32×32×3. The inputdata has data dimensions of 32×32×3. The filter is defined withdimensions of 5×5×3, and is displayed in FIG. 3F in the middle of theinput data. The filter convolves over the image with defined steps,called strides, to process each grouping of input data with the filter.Since in this case the filter only moves one step at a time, the strideis equal to one. The output will be of size 28×28 times the number offilters used. In this example, 5 filters are used. Therefore, the outputwould be 28×28×5. In this first layer, each filter would include 76parameters, resulting from the size 5*5*3 dimensions and one biasparameter. This adds up to a total of 380 parameters used across fivefilters. Training the network would mean changing the values of the 380parameters in a way that the network could better differentiate thedifferent labeled data.

The output of each neuron in CNNs may depend also on the type ofactivation function used. The activation function tanh, discussed above,is converges much more slowly during training, because of the saturatingnonlinearity. ReLUs may show faster training. As a result, it may beadvantageous to use ReLUs as the activation function in some or alllayers, or in some embodiments in all layers except the last layer.

Convolutional layers are often followed by pooling layers, whichdecrease the resolution of the data. One example is shown in FIG. 3G,with pooling layer performing a reduction from 2×2 values to only onevalue, to reduce overall data from dimensions of 4×4 to dimensions of3×3. This is done by choosing the maximum value of the 4 valuesidentified by a pooler. Other pooling techniques take the average of allvalues for reduction. This pattern of convolutional layers and afollowed pooling layer may be repeated several times in architectures ofsome embodiments.

The last layer, called output layer, will flatten the data and use eachdata value as an input for a fully-connected neural network layer, whichis a common layer of a NN, as discussed above. A SoftMax activationfunction may be used to make sure that, for a classification problem,the different prediction likelihoods for different classes sum to 1. Themathematical representation of this method is represented by thefollowing formula:

${\sigma (z)}_{j} = \frac{e^{z_{j}}}{\sum_{k = 1}^{m}e^{z_{k}}}$

FIG. 3H shows an example of a CNN architecture with two convolutionallayers and two pooling layers.

For training a CNN, one technique called mini-batch stochastic gradientdescent may be used. The method can be described in 4 steps that aresequenced in each iteration:

1) Take a sample of data, defined as batch

2) Forward propagate the input through the network to receive the loss

3) Use backpropagation to calculate the gradient

4) Update the parameters by a defined learning rate using the gradient

This procedure is repeated for all batches. Epochs that were alsodefined before set the amount of training the Neural Network may also beused to successively train the network on the input data, with an epochbeing a complete pass of all the training data through the CNN. Inparticular, in each iteration of an epoch, a number of samples oftraining data equal to a batch size are processed. An epoch may includemultiple iterations, to process all of the training data during theepoch. By training the neural network in multiple epochs, the trainingdata may be input to the neural network multiple times.

In some embodiments operating in accordance with techniques describedherein, a CNN may be trained (e.g., using Gradient Descent or anothertechnique) to identify different classes of vehicle data, associatedwith whether a collision occurred and/or with different characteristicsof collisions (e.g., severity, angle of impact, etc.). In some suchembodiments, a batch size of 150 may be chosen, and the CNN may betrained over 200 epochs. The CNN of some embodiments may include aninput layer followed by four sets of layers, where each set includes twoconvolutional layers followed by a pooling layer that reduces the datadimensions by two and a dropout of 25% to mitigate risk of overfitting,and a fully-connected output layer that flattens and combines alldimensions with a SoftMax function to ensure the likelihoods for the setof classes/predictions sum to 1.

CNNs are mostly used in image classification problems. Such networks areconfigured for use with data (e.g., image pixels) with definedpositions. Transferring this thinking to a time series, the time serieshas also values that happen to a certain point in time. This can createa requirement, though, that the time series be uniformly sampled, tohave fixed positions in time. In some embodiments, input data mayinclude a time series with five dimensions (acceleration in x, y, z;magnitude of total acceleration; and speed) which each have values forevery 10ms over a time frame of 20 seconds. The CNN may be trained withthis data to find patterns automatically, which can be used to classifyother data.

When implementing the convolution, it is possible to convolve over thetime series with a one-dimensional filter that will create another timeseries. In some embodiments, the filter may be chosen to have a kernelsize of 5. This means that the filter will consider five steps, which isequal to 50 ms over the time series. Since the input data has fivechannels (the acceleration in x, y, z; the magnitude; and speed), thefilter of this example will be of size 5×5. That means every filtercontains 25 parameters, which may be initialized with a random numberand change through the training process. One formula to calculate thenumber of parameters per layer may be the following:

total_params_per_layer=(filter_width*channels +1) * number_of_filters

The number one that is added inside of the bracket represents a biasthat is included in every neuron.

The process of convolving over the time series is described in FIG. 31for the first convolutional layer. The filter that starts from the leftwith the size 5×5 is doing an elementwise multiplication and writes theresult into the next time series below. With a padding defined as same,the size of the time series that is created will be equal to theoriginal one. This is done by adding zeros to the end of the time seriesand convolving over these as well. By using 18 filters the resultingoutput will be a time series of 2,000 steps with 18 channels.

The number of filters chosen will determine the number of new channelsfor the input of the next layer. In some embodiments, 18 filters may bechosen for the first layer. This means the second convolutional layerwill take a time series as input which consists of 18 dimensions. Usinganother kernel size of 5 will create a new filter which has dimensions5×18. For this filter 90 parameters are trained for each filter.

As discussed above, in some embodiments the CNN architecture may includefour parts, in which each has a one-dimensional convolutional layerfollowed by another one-dimensional convolutional layer withoutdecreasing the step size and, after these two layers, a pooling layerthat decreases the step size by two and addition of a dropout of 25%that prevents the network from overfitting. These four parts areconnected in sequence and feed into a fully connected layer combines alldimensions and a SoftMax returns three predictions for the three definedclasses.

FIG. 4 illustrates one techniques that may be implemented in someembodiments to train a neural network (e.g., a CNN) to implement aclassifier that may be used by a collision detection facility, inaccordance with techniques described herein.

The process 400 of FIG. 4 may be performed during a configuration phase,in which a collision detection facility is configured prior to use suchas in the process 200 of FIG. 2. The configuration may be performed byan administrator, such as a human administrator, setting up thecollision detection system for the subsequent use.

The process 400 of FIG. 4 begins in block 402, in which data describingvehicles moving in normal movements and/or data describing vehiclesengaged in collisions is obtained. Data describing normal movements maybe used to train a classifier to recognize data that is associated withmovements that are not collisions, to define one or more classesassociated with normal driving or otherwise not-collisions. Datadescribing vehicles engaged in collisions may be used to train theclassifier to recognize data that is associated with collisions or withdifferent types of collisions, such as with collisions of differentseverities or collisions with different impact angles. The data that isobtained in block 402 may include acceleration data or other movementsof movement data described above (magnitude of total acceleration,speed), or other data describing a vehicle (e.g., ECU or OBD data). Thedata that is obtained may be for a time period that matches the timeperiod used by the collision detection facility (e.g., that was definedin block 204 of FIG. 2).

The data obtained in block 400 may not be labeled or curated data, andmay not be arranged in a way that a classifier could be clearly trainedon. For example, the data may be mixed together, with data for differentscenarios not labeled or differentiated. To train the classifier, it maybe helpful to at least differentiate and train the data, as there may besome advantages to supervised or semi-supervised training rather thanrelying solely on unsupervised learning.

Accordingly, in block 402, the data describing vehicles engaged incollisions may be separated out in block 404, such that data associatedwith different collision scenarios is separated. This process may bemanual or automatic, depending on what data is available as a basis tofor which to conduct a differentiating. Data associated withnot-collisions may not be differentiated in some embodiments, resultingin one class being trained that has features for all different types ofnormal movements. In other embodiments, though, different classes ofnot-collisions may be defined to aid in more reliably identifyingnot-collisions.

Once the data is separated in block 404, in block 406 the differentcategories of collisions or other information may be labeled withwhether they reflect a collision or characteristics of the type ofcollision they reflect (e.g., severity, angle of impact). In block 408,the labeled data may then be separated into clusters by a machinelearning engine and features identified with each cluster identified bythe machine learning engine, to define the clusters and define theclasses. It is these features that will then be used to subsequentlymatch data for a suspected collision to a class, by looking for apotential match between the data. To repeat an example from above, astraightforward rear-end collision may include movements that areprimarily forward-backward movements, while a collision in which thevehicle is struck broadside by another vehicle may be associated withprimarily right-left movement data. The trained classifier may, based onthe labeled data from block 406, draw these same conclusions byobserving that the rear-end collision is associated largely withforward-backward movement data while a broadside collision is largelyassociated with right-left data.

Based on these learned parameters of each of the clusters, a trainedclassifier is created that includes each of the classes defined from theclusters. The trained classifier may then be used by a collisiondetection facility, such as in the manner described above.

While not discussed above in connection with FIG. 4, it should beappreciated that it would be advantageous if, when movement data isobtained in block 402 that is for a time period, the data would includesamples generated at the same time scale (e.g., same interval), and overthe same period of time, as the data that will be collected by acollision detection facility for a suspected collision. Including thedata for the same length of time period and for the same interval mayease classification of the data input for a suspected collision, as theinput data will be temporally aligned with the training of theclassifier. In some embodiments, the time period may be 20 seconds andthe sampling rate for the movement data may be 10 ms, meaning there are100 samples per second.

In some cases, the data that is obtained in block 402 to be used intraining a system may not be aligned in this manner with the data thatwill be collected by a collision detection facility. For example, thedata may be for a shorter or longer time period, or may include samplesgenerated at a different interval. The movement data may therefore beprepared for training in some embodiments, by generating data for thesame time period and at the same interval. If in the input data is for alonger time period, the data may be truncated to match the desired timeperiod length. If the time period is shorter, additional data may begenerated by interpolating the available data. As another example, ifthe input data includes samples at a different interval than the datawill later be collected by a collision detection facility, such as aslower or faster sampling rate, the input data will be sampled and/orinterpolated to generate data at the desired time intervals and for thedesired time period. For example, data points in the input data that areadjacent to a time at which a data point is desired (for a time thataligns with the desired sampling rate) will be interpolated to generatethe desired data point.

In some embodiments, Dynamic Time Warping (DTW) is used to process inputtime series data and prepare it for analysis using the trainedclassifier.

In some embodiments, the magnitudes of input data points may also benormalized based on the scale of input training data, to yield valuesthat are on a similar scale.

In this manner, a classifier can be trained based on collision data togenerate information on whether a collision has occurred and, if so, oneor more characteristics of that collision.

FIGS. 5A-5E illustrate examples of data with which some embodiments mayoperate, as an example of how techniques described herein may beimplemented in some embodiments.

FIGS. 5A-5C illustrate examples of graphs of movement data that may beused to train a classifier or that may be compared to a trainedclassifier to determine whether a collision occurred and/or tocharacterize the collision. The example of FIG. 5A is associated withcollisions in which a vehicle is rear-ended or hits an object (e.g.,another vehicle, or an obstacle) straight on with its front end. Theseare forward-backward collisions. The graph of FIG. 5A includes lines foraccelerations in x (forward-backward), y (right-left), and z (up-down)directions for the vehicle, as well as a magnitude of total accelerationat a time (“vec”), and a speed v. The x-axis of this graph is a timeperiod for a collision, such as the time period discussed above inconnection with block 204 of FIG. 2, with the time between each pointrepresenting the sampling interval. As can be seen from the graphs ofthe x, y, and z accelerations, the collision included little y(right-left) or z (up-down) acceleration and instead largely consists ofnegative acceleration (deceleration) in the x direction(forward-backward). This is because, in a typical forward-backwardcollision like a rear-ending, most of the acceleration change is in theforward-backward direction. The classifier may learn that pattern andsubsequently identify situations in which the change in acceleration islargely in the x direction as forward-backward collisions like arear-ending.

FIG. 5B illustrates an example of a graph in which a vehicle was struckfrom an angle, not forward-backward. The graph of FIG. 5B includes thesame lines for the same variables as the graph of FIG. 5A. The graph ofFIG. 5B demonstrates that in angled collisions, there is change inacceleration in both the x direction (forward-backward) and the ydirection (right-left), though little change in the z direction(up-down).

FIG. 5C illustrates an example of a graph in which a vehicle, as part ofa collision, left the road. The graph of FIG. 5C includes the same linesfor the same variables as the graphs of FIGS. 5A and 5B. As can be seenin the graph of FIG. 5C, when the vehicle leaves the road, there may besubstantial changes not only in the x and y directions, but also in thez direction (up-down). In each of these lines, there is substantialchange over time, showing that the vehicle moved quite a bit as part ofthis accident.

Data for different forms of collisions is illustrated in FIG. 5D,formatted as a scatterplot based on the x and y accelerations with colorof the dot indicating accident type and size of the dot indicatingspeed. A collection of bright blue dots is spread throughout the middleof the graph, cluster in a band around the 0 value on the y-axis butspread throughout the x axis. These are dots associated with differentforward-backward collisions, confirming again that a forward-backwardcollision has little change in the y direction (right-left). The greendots, however, show varying values for change in the y direction(right-left) and are all associated with negative change (deceleration)in the x direction (forward-backward). These are points associated withangled impacts, where there is substantial change in the right-leftdirection and the vehicle slows down substantially in theforward-backward direction.

As discussed above, attempting to detect or characterize a collisionusing only acceleration data from a single point during an accident isunreliable. Using techniques described herein, which obtain longitudinalmovement information for a time period surrounding an event associatedwith a suspected collision, or other information obtained for a vehiclefor that time period, may be highly reliable. FIG. 5E shows a chartdemonstrating this high reliability, with low false positions or falsenegatives. In the chart, a “1” category is not a collision, a “2”category is a forward-backward collision, and a “3” category is anangled impact collision. The graph shows the predictions generated by asystem trained using the data of FIG. 5D, compared with the actualscenario. As shown by the chart, the trained classifier will accuratedetermine, in 98.7% of cases, that no collision occurred when there was,in reality, no collision. The classifier never identifies data relatedto a not-collision as reflecting an angled-impact collision, and only1.3% of the time, incorrectly identifies the not-collision as being aforward-backward collision. Similarly, the trained classifier properlyidentifies, in 95.6% of cases, that a forward-backward collision is aforward-backward collision, with the remaining cases limited tomisidentifying the scenario as a not-collision. Lastly, the trainedclassifier correctly concludes, in 98.2% of cases, that an angled-impactcollision is an angled-impact collision, with the misidentificationsevenly spread, in less than 1% of cases, between not-collisions andforward-backward collisions.

Accordingly, the trained system is highly reliable in determiningwhether a collision occurred and, if so, in characterizing an angle ofimpact of the collision. The collision detection facility of someembodiments described herein can therefore be reliably used to determinewhether a collision has occurred and, if so, characteristics of thecollision, to determine an appropriate response to the collision.

Techniques operating according to the principles described herein may beimplemented in any suitable manner. Included in the discussion above area series of flow charts showing the steps and acts of various processesthat determine whether a collision occurred and/or, if so, tocharacterize a collision. The processing and decision blocks of the flowcharts above represent steps and acts that may be included in algorithmsthat carry out these various processes. Algorithms derived from theseprocesses may be implemented as software integrated with and directingthe operation of one or more single- or multi-purpose processors, may beimplemented as functionally-equivalent circuits such as a Digital SignalProcessing (DSP) circuit or an Application-Specific Integrated Circuit(ASIC), or may be implemented in any other suitable manner. It should beappreciated that the flow charts included herein do not depict thesyntax or operation of any particular circuit or of any particularprogramming language or type of programming language. Rather, the flowcharts illustrate the functional information one skilled in the art mayuse to fabricate circuits or to implement computer software algorithmsto perform the processing of a particular apparatus carrying out thetypes of techniques described herein. It should also be appreciatedthat, unless otherwise indicated herein, the particular sequence ofsteps and/or acts described in each flow chart is merely illustrative ofthe algorithms that may be implemented and can be varied inimplementations and embodiments of the principles described herein.

Accordingly, in some embodiments, the techniques described herein may beembodied in computer-executable instructions implemented as software,including as application software, system software, firmware,middleware, embedded code, or any other suitable type of computer code.Such computer-executable instructions may be written using any of anumber of suitable programming languages and/or programming or scriptingtools, and also may be compiled as executable machine language code orintermediate code that is executed on a framework or virtual machine.

When techniques described herein are embodied as computer-executableinstructions, these computer-executable instructions may be implementedin any suitable manner, including as a number of functional facilities,each providing one or more operations to complete execution ofalgorithms operating according to these techniques. A “functionalfacility,” however instantiated, is a structural component of a computersystem that, when integrated with and executed by one or more computers,causes the one or more computers to perform a specific operational role.A functional facility may be a portion of or an entire software element.For example, a functional facility may be implemented as a function of aprocess, or as a discrete process, or as any other suitable unit ofprocessing. If techniques described herein are implemented as multiplefunctional facilities, each functional facility may be implemented inits own way; all need not be implemented the same way. Additionally,these functional facilities may be executed in parallel and/or serially,as appropriate, and may pass information between one another using ashared memory on the computer(s) on which they are executing, using amessage passing protocol, or in any other suitable way.

Generally, functional facilities include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Typically, the functionalityof the functional facilities may be combined or distributed as desiredin the systems in which they operate. In some implementations, one ormore functional facilities carrying out techniques herein may togetherform a complete software package. These functional facilities may, inalternative embodiments, be adapted to interact with other, unrelatedfunctional facilities and/or processes, to implement a software programapplication

Some exemplary functional facilities have been described herein forcarrying out one or more tasks. It should be appreciated, though, thatthe functional facilities and division of tasks described is merelyillustrative of the type of functional facilities that may implement theexemplary techniques described herein, and that embodiments are notlimited to being implemented in any specific number, division, or typeof functional facilities. In some implementations, all functionality maybe implemented in a single functional facility. It should also beappreciated that, in some implementations, some of the functionalfacilities described herein may be implemented together with orseparately from others (i.e., as a single unit or separate units), orsome of these functional facilities may not be implemented.

Computer-executable instructions implementing the techniques describedherein (when implemented as one or more functional facilities or in anyother manner) may, in some embodiments, be encoded on one or morecomputer-readable media to provide functionality to the media.Computer-readable media include magnetic media such as a hard diskdrive, optical media such as a Compact Disk (CD) or a Digital VersatileDisk (DVD), a persistent or non-persistent solid-state memory (e.g.,Flash memory, Magnetic RAM, etc.), or any other suitable storage media.Such a computer-readable medium may be implemented in any suitablemanner, including as computer-readable storage media 606 of FIG. 6described below (i.e., as a portion of a computing device 600) or as astand-alone, separate storage medium. As used herein, “computer-readablemedia” (also called “computer-readable storage media”) refers totangible storage media. Tangible storage media are non-transitory andhave at least one physical, structural component. In a“computer-readable medium,” as used herein, at least one physical,structural component has at least one physical property that may bealtered in some way during a process of creating the medium withembedded information, a process of recording information thereon, or anyother process of encoding the medium with information. For example, amagnetization state of a portion of a physical structure of acomputer-readable medium may be altered during a recording process.

In some, but not all, implementations in which the techniques may beembodied as computer-executable instructions, these instructions may beexecuted on one or more suitable computing device(s) operating in anysuitable computer system, including the exemplary computer system ofFIG. 1, or one or more computing devices (or one or more processors ofone or more computing devices) may be programmed to execute thecomputer-executable instructions. A computing device or processor may beprogrammed to execute instructions when the instructions are stored in amanner accessible to the computing device or processor, such as in adata store (e.g., an on-chip cache or instruction register, acomputer-readable storage medium accessible via a bus, acomputer-readable storage medium accessible via one or more networks andaccessible by the device/processor, etc.). Functional facilitiescomprising these computer-executable instructions may be integrated withand direct the operation of a single multi-purpose programmable digitalcomputing device, a coordinated system of two or more multi-purposecomputing device sharing processing power and jointly carrying out thetechniques described herein, a single computing device or coordinatedsystem of computing devices (co-located or geographically distributed)dedicated to executing the techniques described herein, one or moreField-Programmable Gate Arrays (FPGAs) for carrying out the techniquesdescribed herein, or any other suitable system.

FIG. 6 illustrates one exemplary implementation of a computing device inthe form of a computing device 600 that may be used in a systemimplementing techniques described herein, although others are possible.It should be appreciated that FIG. 6 is intended neither to be adepiction of necessary components for a computing device to operate acollision detection facility in accordance with the principles describedherein, nor a comprehensive depiction.

Computing device 600 may comprise at least one processor 602, a networkadapter 604, and computer-readable storage media 606. Computing device600 may be, for example, a desktop or laptop personal computer, apersonal digital assistant (PDA), a smart mobile phone, a server, or anyother suitable computing device. Network adapter 604 may be any suitablehardware and/or software to enable the computing device 600 tocommunicate wired and/or wirelessly with any other suitable computingdevice over any suitable computing network. The computing network mayinclude wireless access points, switches, routers, gateways, and/orother networking equipment as well as any suitable wired and/or wirelesscommunication medium or media for exchanging data between two or morecomputers, including the Internet. Computer-readable media 606 may beadapted to store data to be processed and/or instructions to be executedby processor 602. Processor 602 enables processing of data and executionof instructions. The data and instructions may be stored on thecomputer-readable storage media 606.

The data and instructions stored on computer-readable storage media 606may comprise computer-executable instructions implementing techniqueswhich operate according to the principles described herein. In theexample of FIG. 6, computer-readable storage media 606 storescomputer-executable instructions implementing various facilities andstoring various information as described above. Computer-readablestorage media 606 may store a collision detection facility 608, atrained classifier 610 for the facility 608 (including definitions ofclasses for the classifier), and data 612 that includes vehicle data andcollision data, which may be collected for a suspect collision andanalyzed by the collision detection facility 608 and/or used to trainthe classifier 610 for subsequent use in analyzing data regarding asuspect collision.

While not illustrated in FIG. 6, a computing device may additionallyhave one or more components and peripherals, including input and outputdevices. These devices can be used, among other things, to present auser interface. Examples of output devices that can be used to provide auser interface include printers or display screens for visualpresentation of output and speakers or other sound generating devicesfor audible presentation of output. Examples of input devices that canbe used for a user interface include keyboards, and pointing devices,such as mice, touch pads, and digitizing tablets. As another example, acomputing device may receive input information through speechrecognition or in other audible format.

Embodiments have been described where the techniques are implemented incircuitry and/or computer-executable instructions. It should beappreciated that some embodiments may be in the form of a method, ofwhich at least one example has been provided. The acts performed as partof the method may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

Various aspects of the embodiments described above may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

The word “exemplary” is used herein to mean serving as an example,instance, or illustration. Any embodiment, implementation, process,feature, etc. described herein as exemplary should therefore beunderstood to be an illustrative example and should not be understood tobe a preferred or advantageous example unless otherwise indicated.

Having thus described several aspects of at least one embodiment, it isto be appreciated that various alterations, modifications, andimprovements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be part ofthis disclosure, and are intended to be within the spirit and scope ofthe principles described herein. Accordingly, the foregoing descriptionand drawings are by way of example only.

What is claimed is:
 1. A method comprising: detecting an accelerationevent from an accelerometer included in a monitoring device that isinstallable in a vehicle, the acceleration event being indicative of apotential collision between the vehicle and an object; setting a timeperiod from before and after the detected acceleration event; collectingadditional data, other than the acceleration event, obtained during thetime period; sending at least the additional data to a remote sitehaving servers operated by a vendor of the monitoring device and that isremote from the vehicle for subsequent analysis; and, providing, fromthe remote site, the at least the additional data to an operator of afleet of vehicles of which the vehicle is a member.
 2. The method ofclaim 1, wherein setting a time period from before and after thedetected acceleration event comprises setting a time period from beforeand after the detected acceleration event in response to the detectedacceleration event.
 3. The method of claim 1, further comprisingcommunicating, via Bluetooth®, at least the additional data from themonitoring device to another device for sending to the remote site. 4.The method of claim 1, further comprising determining whether thepotential collision is likely to have been a collision based on thedetected acceleration event and the additional data.
 5. The method ofclaim 1, further comprising contacting a driver of the vehicle.
 6. Themethod of claim 1, wherein setting a time period from before and afterthe detected acceleration event comprises setting a time period between3 and 10 seconds.
 7. A method comprising: detecting an accelerationevent from an accelerometer included in a monitoring device that isinstallable in a vehicle, the acceleration event being indicative of apotential collision between the vehicle and an object; setting a timeperiod from before and after the detected acceleration event; collectingadditional data, other than the acceleration event, obtained during thetime period; and sending at least the additional data to a remote sitethat is remote from the vehicle for subsequent analysis.
 8. The methodof claim 7, wherein setting a time period from before and after thedetected acceleration event comprises setting a time period from beforeand after the detected acceleration event in response to the detectedacceleration event.
 9. The method of claim 7, further comprisingproviding, from the remote site, at least the additional data to anoperator of a fleet of vehicles of which the vehicle is a member. 10.The method of claim 7, wherein sending at least the additional data to aremote site comprises sending the at least the additional data toservers operated by a vendor of the monitoring device.
 11. The method ofclaim 7, further comprising communicating, via Bluetooth®, at least theadditional data from the monitoring device to another device for sendingto the remote site.
 12. The method of claim 11, further comprisingproviding, from the remote site, at least the additional data to anoperator of a fleet of vehicles of which the vehicle is a member. 13.The method of claim 7, further comprising notifying, from the remotesite, an operator of a fleet of vehicles of which the vehicle is amember that a collision is likely to have occurred.
 14. The method ofclaim 7, further comprising determining whether the vehicle hasexperienced an actual collision.
 15. The method of claim 7, furthercomprising determining whether the potential collision is likely to havebeen a collision based at least in part on the additional data.
 16. Themethod of claim 15, further comprising determining whether the potentialcollision is likely to have been a collision based at least in part onthe detected acceleration event.
 17. The method of claim 7, furthercomprising contacting a driver of the vehicle.
 18. The method of claim17, further wherein contacting the driver of the vehicle comprisesautomatically contacting the driver of the vehicle.
 19. The method ofclaim 7, further comprising contacting emergency services and/orroadside assistance services in an area in which the vehicle is locatedto request dispatch of emergency services or roadside assistance to thevehicle using location information and/or identifying information of thevehicle.
 20. The method of claim 7, wherein collecting additional datacomprises collecting a speed of the vehicle.
 21. The method of claim 7,wherein collecting additional data comprises data indicative of movementof the vehicle.
 22. The method of claim 7, wherein collecting additionaldata comprises collecting data regarding one or more components of thevehicle.
 23. The method of claim 22, wherein collecting data regardingone or more components of the vehicle comprises collecting data from anairbag sensor.
 24. The method of claim 7, wherein setting a time periodfrom before and after the detected acceleration event comprises settinga time period between 3 and 10 seconds.
 25. The method of claim 7,further comprising classifying, using at least one trained classifier,the acceleration event and the additional data into at least one of aplurality of classes, each class of the plurality of classes beingassociated with whether a collision occurred, and determining whetherthe potential collision is likely to have been an actual collision basedat least in part on the at least one class identified in theclassifying.
 26. The method of claim 25, wherein each class of theplurality of classes that is associated with occurrence of a collisionis further associated with at least one collision characteristic; andwherein the method further comprises, in response to determining thatthe potential collision is likely to have been a collision,characterizing the collision based at least in part on the at least onecollision characteristic associated with one or more of the at least oneclass identified in the classifying.
 27. The method of claim 22, whereinobtaining information generated by one or more components of the vehiclecomprises obtaining information via an On-Board Diagnostics (OBD) systemof the vehicle.
 28. At least one non-transitory computer-readablestorage medium having encoded thereon executable instructions that, whenexecuted by at least one processor, cause the at least one processor tocarry out a method comprising: detecting an acceleration event from anaccelerometer included in a monitoring device that is installable in avehicle, the acceleration event being indicative of a potentialcollision between the vehicle and an object; setting a time period frombefore and after the detected acceleration event; collecting additionaldata, other than the acceleration event, obtained during the timeperiod; and sending at least the additional data to a remote site thatis remote from the vehicle for subsequent analysis.
 29. An apparatuscomprising: a monitoring device that is installable in a vehicle, themonitoring device having at least one processor, an accelerometercoupled to the at least one processor, and at least one storage mediumcommunicating with the processor and having encoded thereon executableinstructions that, when executed by the at least one processor, causethe at least one processor to carry out a method comprising: detectingan acceleration event from the accelerometer, the acceleration eventbeing indicative of a potential collision between the vehicle and anobject; setting a time period from before and after the detectedacceleration event; collecting additional data, other than theacceleration event, obtained during the time period; and sending atleast the additional data to a remote site that is remote from thevehicle for subsequent analysis.